Historical Failure Rates Research Articles

Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

The clinical care process for people with prediabetes starts with lifestyle intervention, often escalating to more intense treatment due to the low success rate of the first-line intervention. Clinicians lack clear guidelines on which patients would benefit from early treatment with more intensive therapeutic options, so we aimed to develop an algorithm to early identify non-responders to lifestyle intervention for prediabetes. Several statistical and machine learning algorithms were screened with internal cross-validation on the basis of accuracy and discrimination ability to correctly classify patients that would fail to normalize fasting glycemia within one year of being prescribed a lifestyle intervention, solely based on the first examination measurements. Of the many screened algorithm, only a random forest model performed with sufficient accuracy to exceed the historical failure rate of patients within our center, with an accuracy of 0.689 (CI 0.669, 0.710) and an AUROC of 0.687 (CI 0.673, 0.701). This study showcases the ability of machine learning models to provide useful insight in clinical practice leveraging knowledge contained in routinely collected data.

Multifactor evaluation method of smart meter

As an important terminal of smart grid, smart meter has the functions of equipment health monitoring and power metering. However, it is difficult to quantify the impact of factors such as ex-factory parameters, historical failure rates, electromagnetic interference when conducting an operational condition assessment. Therefore, in view of the large number of influencing factors and the difficulty in quantifying the degree of influence, an improved multifactor evaluation model for the operating status of smart meters is constructed. Firstly, a multifactor index system for evaluating the operating state of smart meters is constructed by analyzing the working mechanism and operating characteristics of smart meters. Then, an improved combinatorial weighting method is proposed by combining the subjective weight and the objective weight. The combinatorial weighting method is used to estimate the weight of factors in the health status evaluation of smart meters, and the improved multifactor evaluation method is presented to evaluate the health status of smart meters. Finally, an example is given to verify and analyze the proposed method. Compared with the other three methods, this method can provide effective evaluation results, and help guide targeted maintenance or replacement to improve the efficiency of smart meter detection.

Selecting conduits for coronary artery bypass grafting: caution regarding the right internal mammary artery

This narrative review summarizes the angiographic and clinical outcome results of the most common coronary artery bypass grafting (CABG) conduits. The left internal mammary artery is the preferred first conduit to bypass the left anterior descending artery due to superior long-term survival and graft patency. Recent studies suggest the radial artery may be the preferred second conduit for the circumflex or right coronary artery territories, challenging the belief that the right internal mammary artery is the best choice. Despite their historical high failure rates, saphenous vein grafts continue to be widely used as secondary conduits. Several recent studies report suboptimal rates of right internal mammary artery graft failure, with clinical outcomes comparable to or worse than saphenous veins. The suboptimal rates of RIMA graft failure may be attributed to several factors such as improvements in vein graft failure rates, the use of in situ and non-left anterior descending artery grafting configurations, and skeletonized harvesting techniques. While observational studies favor multiple over single arterial grafting, randomized studies are needed for confirmation. The ongoing Randomized comparison of the clinical Outcome of single vs. Multiple Arterial grafts (ROMA) trial aims to determine if multiple arterial grafting reduces major adverse cardiovascular events and mortality and how secondary conduit selection influences these outcomes. Greater adoption of arterial grafting strategies is likely to come from high-quality evidence of benefit and safety from ongoing and future large pragmatic trials.

Anterior Cruciate Ligament Injury: What Are Trends in Treatment?

Summary. There has been a reemergence of investigation into Anterior Cruciate Ligament (ACL) repair as a surgical option for patients presenting with an ACL rupture. Despite high historical failure rates, new innovations have been implemented to improve stability and biointegration in an effort to aid healing of the ligament and decrease failure rates following ACL repair. The evolution of surgical techniques has seemingly led to improved short-term outcomes over historical reports. However, there remains a paucity of literature on long-term outcomes, large series, and comparison among repair techniques. Furthermore, there have been no high-quality studies showing outcomes comparable with those after ACL reconstruction. While advancements in ACL repair have been encouraging, there are no data, to our knowledge, to support ACL repair over ACL reconstruction, as recent reports have shown high failure rates after ACL repair. For these reasons, at the present time, ACL reconstruction should remain the gold standard treatment for patients with functional impairment from an ACL rupture. In the future, there may be a role for ACL repair for select tear types in specific individuals, if the early ACL repair outcome studies produce consistent long-term outcomes comparable with those after ACL reconstruction.

IMPLEMENTATION OF MAINTENANCE AND RELIABILITY MANAGEMENT SYSTEM (MRMS) IN PERTAMINA HULU ENERGY SUBHOLDING UPSTREAM (PHE SHU) THROUGH FIELD ASSESSMENT OF ISO 55001

As part of the integrated efforts that need to be made to achieve the target of becoming a world-class energy company, PHE SHU seeks to carry out the implementation of the Asset Integrity Management System (AIMS) and develop a MRMS which involves a solid team and is able to complete implementation in a timely, effective, efficient and highly competitive manner, to achieve a production target of 1 million bbl/day and 12 bcf/day in 2030 for national energy security referring to the international standard ISO 55001. MRMS is a reference for the governance of the reliability program and maintenance of production facility assets in the Upstream Subholding environment in the operational phase of the Pertamina Group asset management life cycle. identification of Failure Mode Effect Analysis (FMEA) preparation of maintenance strategy programs in the form of task selection for failure mode control based on studies in FMEA namely preparation of detection work programs in rotating equipment in the form of Predictive Maintenance (PdM) activities, task selection of detection programs in static equipment in the form of RBl activity. Task selection prevention in the form of Preventive Maintenance (PM) activities. Failure finding (function test), including determining time intervals for carrying out inspection activities for the PdM, RBI, PM, function test, ORDC (Operator Routine Duty Check List) programs based on the historical failure rate stated in PAREDA in ensuring that Upstream Oil and Gas Assets are optimized, sustainable, safe & reliable operations.

Improved Modeling of Maintenance for Future Vertical Lift

The Future Vertical Lift (FVL) Cross Functional Team (CFT) required analysis on the sustainment performance of FVL aircraft operating in Multi-Domain Operations (MDO). A study from last year forecasted the sustainment performance of FVL’s Future Attack Reconnaissance Aircraft (FARA) based upon historical performance of the enduring AH-64 Apache infused with emerging technologies. This study improved last year’s model by refining key assumptions. First, a Forward Maintenance Team in MDO conducted limited maintenance that was constrained by an austere environment. Second, the assumption of constant component failure rates was updated with increasing hazard rates to reflect fatigue in mechanical systems like the transmission and rotors. Third, technological risk was captured by accounting for the uncertainty associated with infused technologies based on the Technology Readiness Level. Finally, the historical failure rates of the enduring fleet of AH-64, UH-60, and CH-47 aircraft guided reliability forecasts for FARA increments over its life cycle.

Reliability calculations based on an enhanced transformer life expectancy model

In this paper, The IEEE thermal ageing model is utilized to build an enhanced Transformer Life Expectancy (TLE) model. The enhanced model is developed relying on the current transformer Health Index (HI) in addition to the loading conditions. A direct correlation between the IEEE and the (HI) ageing factors is identified and projected. The model is capable of integrating factors affecting transformers. Transformer faults and maintenance are amongst factors taken into consideration to improve the model. The reliability calculations are applied for assessing the transformers as well as the distribution network. The calculated transformer (HI) score with the aid of historical failure rate are utilized during the calculation of the distribution network reliability. An application on a simple network connectivity including transformers and loads is demonstrated to verify the strategy effectiveness. The results of this research are the basis for an enhancement for the smart grids performance estimation.

25-OR: Machine Learning for Early Detection of Nonresponders to Lifestyle Intervention for Prediabetes

Introduction: Lifestyle intervention is the first-line treatment for prediabetes. More intensive intervention usually follows for those patients that fail to improve glycemia. We explored the possibility to early detect non-responders to lifestyle intervention from a single baseline evaluation. Methods: The sample was composed of 738 pre-diabetic (fasting glucose 100-125 mg/dl) patients (median age 54 IQR 45-62, 57% female, median BMI 30.4 IQR 27.5, 34.2) , that were prescribed a lifestyle intervention, and were re-evaluated within 12 months. Lifestyle intervention consisted of a hypocaloric diet (based on resting energy expenditure measured by indirect calorimetry, with a Mediterranean pattern, tailored on patient habits) and WHO physical activity guidelines. The outcome was normalization of fasting glucose at follow-up (0: ≥100 mg/dl, 1: <100 mg/dl) , with an historical failure rate of 44%. Baseline evaluation consisted of anthropometry, indirect calorimetry, abdominal fat ultrasound, laboratory exams, structured medical and nutritional interview, and psychological questionnaires, with a total of 114 predictors. Statistical and machine learning models were screened to select the best classifier for the outcome. Results: The best model was a random forest model (a machine learning model) , with a validation-set accuracy of 72%, ROC AOC of 0.64, sensitivity of 55%, and specificity of 86%. Discussion: The best developed model is able to identify more than half non-responders on the basis of a single baseline evaluation, while including few responders as false-positives. The model accuracy resulted higher than the historical rate of failure, so integrating the model suggestion in the clinical decision process should allow to more promptly treat future patients and improve the overall success rate. Machine learning models show potential in leveraging high-dimensional multi-domain routinely-collected clinical data to aid in clinical decision. Disclosure A.Foppiani: None. R.De amicis: None. A.Leone: None. S.Bertoli: None. A.Battezzati: None.

Design for Reliability: Experimental and Numerical Simulation of Cased and Perforated Completions with Standalone Screen

Summary The historical challenges and high failure rate of using standalone screen in cased and perforated wellbores pushed several operators to consider cased-hole gravel packing or frac packing as the preferred completion. Despite the reliability of these options, they are more expensive than a standalone screen completion. In this paper, we employ a combined physical laboratory testing and computational fluid dynamics (CFD) for laboratory scale and field scale to assess the potential use of the standalone screen in completing the cased and perforated wells. The aim is to design a fit-to-purpose sand control method in cased and perforated wells and provide guidelines in perforation strategy and investigate screen and perforation characteristics. More specifically, the simultaneous effect of screen and perforation parameters, near wellbore conditions on pressure distribution and pressure drop are investigated in detail. A common mistake in completion operation is to separately focus on the design of the screen based on the reservoir sand print and design of the perforation. If sand control is deemed to be required, the perforation strategy and design must go hand in hand with sand control design. Several experiments and simulation models were designed to better understand the effect of perforation density, the fill-up of the annular gap between the casing and screen, perforation collapse, and formation and perforation damage on pressure drop. The experiments consisted of a series of step-rate tests to investigate the role of fluid rate on pressure drop and sand production. There is a critical rate at which the sand filling up the annular gap will fluidize. Both test results and CFD simulation scenarios are comparatively capable to establish the relation between wellbore pressure drop and perforation parameters and determine the optimized design. The results of this study highlight the workflow to optimize the standalone screen design for the application in cased and perforated completions. The proper design of standalone screen and perforation parameters allows maintaining cost-effective well productivity. Results of this work could be used for choosing the proper sand control and perforation strategy.

Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather

This paper presents a probabilistic methodology for assessing power system resilience, motivated by the extreme weather storm experienced in Iceland in December 2019. The methodology is built on the basis of models and data available to the Icelandic transmission system operator in anticipation of the said storm. We study resilience in terms of the ability of the system to contain further service disruption, while potentially operating with reduced component availability due to the storm impact. To do so, we develop a Monte Carlo assessment framework combining weather-dependent component failure probabilities, enumerated through historical failure rate data and forecasted wind-speed data, with a bi-level attacker-defender optimization model for vulnerability identification. Our findings suggest that the ability of the Icelandic power system to contain service disruption moderately reduces with the storm-induced potential reduction of its available components. In other words, and as also validated in practice, the system is indeed resilient.

Еще раз о цикличности аварий в основных сетях энергосистем

The recurrence of failures of 500 kV overhead lines in a vast region on an extended time interval is investigated. An essential scatter of their flaw rate (failure frequency) values under the effect of natural and social-economic factors has been revealed. The failure frequency wavelet spectra were plotted, from which a few historical failure rate periods have been revealed. It is proposed to consider the flaw rate as the output signal of a dynamic system containing a multitude of poorly formalized inputs. This parameter is determined by multifactorial and poorly formalized summation of the effects caused by the environment and by social-economic relations. The stability of this dynamic system has been estimated. Ways for formalizing the multidimensional dynamic model of failure rate in the grid backbone networks have been outlined.

Risk Assessment of Switchgears in Distribution System Considering Environmental Dependency

As a link between power system and users, the distribution network plays an important role. Therefore, the power quality and reliability of distribution system are of main significance to both users and power system. The switchgears are one of the most widely used components in the distribution network. Consequently, it is necessary to study the reliability of the distribution switchgears. Traditional researches on risk assessment of switchgears hardly considered the potential threat caused by high temperature, high pressure and humid environment, which is quite common in some areas especially the coastal areas of distribution network. In order to forecast the failure rates of switchgears better, the method for failure rate forecasting considering the environmental factors based on Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) are advanced in this paper. The related ambient temperature, humidity and the historical failure rate which were obtained from the actual operation distribution data from China Southern Power Grid (CSPG) are normalized firstly. And then create the training function to train the processed data. Next the BPNN and SVR are used to minimize the error between the predicted value and the actual value through iteration. A demonstration based on a coastal city in China is given to prove the validity of the algorithm. The environmental dependency result is compared with the result from the method that only the historical failure rate considered. A general conclusion can be drawn that the SVR method is more accurate than the BPNN method. And it is expected to be widely used in the whole CSPG in the future.

Short-Term Line Maintenance Scheduling of Distribution Network With PV Penetration Considering Uncertainties

In this paper, the methodology for short-term line maintenance scheduling in distribution network with PV penetration is proposed, in which the mixed randomness and fuzziness of solar power generation concerning the available cloud amount forecasting, along with other random or fuzzy factors, such as electricity demand and component historical failure rate, are considered. First, based on the obtained historical data from NASA, the empirical mapping from cloud amount to solar irradiance can be established where the uncertainty is represented by combining randomness and fuzziness. Second, the short-term line maintenance scheduling of distribution network with uncertainties is modeled by using random fuzzy chance-constrained programming aiming at minimizing the pessimistic value in terms of economics and reliability subject to the chance constraints. Finally, a hybrid intelligent algorithm with two-layer optimization is implemented to solve the model. Simulation experiments are carried out on the IEEE 33-Bus and IEEE RBTS 2-Bus systems, and the results demonstrate the effectiveness of the proposed short-term line maintenance scheduling solution for distribution network with the mixed uncertainties of randomness and fuzziness.

Reliability modelling and assessment of electric motor driven systems in hydrocarbon industries

Reliability is a significant performance index for any electrical equipment in the industry, especially for electric motor driven (EMD) systems. Modelling the reliability of an EMD system is a difficult and challenging task. However, based on field data, the dominant failure modes of some motor system parts can be used to develop a simple and credible reliability model. This study proposes a failure rate prediction model for an EMD system that is currently in use in hydrocarbon industries. In addition, a fault tree model was designed to identify the root causes of the failures and the failure mechanism of the most dominates failure modes in EMD systems. To accomplish the study's objectives, an existing failure databank of over 100 failures of EMD systems was collected and surveyed from the field. The historical information and failure rate models that were collected for the electric motors are investigated and used to model the prediction model of the EMD system. Three competing failure modes were found to dominate motor life: bearing failures, winding failures and shaft failures. A mathematical model for each of these failure modes with dominant variables is developed, and a risk failure model is established.

Clustering Empirical Failure Rate Curves for Reliability Prediction Purposes in the Case of Consumer Electronic Products

In this paper, a methodology based on the combination of time series modeling and soft computational methods is presented to model and forecast bathtub‐shaped failure rate data of newly marketed consumer electronics. The time‐dependent functions of historical failure rates are typified by parameters of an analytic model that grabs the most important characteristics of these curves. The proposed approach is also verified by the presentation of an industrial application brought along at an electrical repair service provider company. The prediction capability of the introduced methodology is compared with moving average‐based and exponential smoothing‐based forecasting methods. According to the results of comparison, the presented method can be considered as a viable alternative reliability prediction technique. Copyright © 2015 John Wiley & Sons, Ltd.

Analysis of the Implied Reliability Index and Load and Resistance Coefficients for China’s Current Highway Bridge Load-Carrying Capacity Evaluation Code

China’s current load-carrying capacity evaluation code for highway bridges is a semi reliability-based code. The reliability level of the code is analyzed through a comparative study, and methods used to update the reliability level of this code are specified in this paper. A formula to calculate the implied reliability index is proposed and verified by comparison with calibrated reliability indexes in design codes. Then the formula is used to calculate the implied reliability index with statistical parameters derived from the load and resistance coefficients in the current code. It is proposed that the implied reliability index should be adjusted by considering historical failure rates. Different criteria used to define historical failure rates of highway bridges are discussed. In addition, the paper compares the reliability level, applicability, evaluation frequency, target reliability indexes, and resistance and load coefficients between AASHTO and China’s current code. The paper would serve as a reference for the upcoming research on the updating of current highway bridge Load-carrying capacity evaluation code.

A risk‐based approach to manage non‐repairable spare parts inventory

Purpose – The purpose of this paper is to propose a risk‐based approach for spare parts demand forecast and spare parts inventory management for effective allocation of limited resources. Design/methodology/approach – To meet the availability target and to reduce downtime, process facilities usually maintain inventory of spare parts. The maintaining of non‐optimized spare parts inventory claims more idle investment. Even if it is optimized, lack of attention towards the critical equipment spares could threaten the availability of the plant. This paper deals with the various facets of spare parts inventory management, mainly risk‐based spare parts criticality ranking, forecasting, and effective risk reduction through strategic procurement policy to ensure spare parts availability. A risk‐based approach is presented that helps managing spare parts requirement effectively considering the criticality of the components. It also helps ensuring the adequacy of spare parts inventory on the basis of equipment criticality and dormant failure without compromising the overall availability of the plant. Findings – The paper proposes a risk‐based approach that used conjugate distribution technique with the capability to incorporate historical failure rate as well as expert judgment to estimate the future spare demand through posterior demand distribution. The approach continuously updates the prior distribution with most recent observation to give posterior demand distribution. Hence the approach is unique in its kind. Practical implications – Appropriate spare parts unavailability could have great impact on process operation and result in costly downtime of the plant. Following proposed approach the availability target can be achieved in process industry having limited maintenance resources, by forecasting spare parts demand precisely and maintaining inventory in good condition. Originality/value – Adopting the approach proposed in the paper, risk level can be minimized and plant availability can be maximized within the financial constraint. The resources are allocated to the most critical components and thereby increased availability, and reduce risk.

Revisiting Retirement Withdrawal Plans and Their Historical Rates of Return

This paper examines the historical record of the so-called 4% rule, the popular guideline for sustainable real annual withdrawals in a self funded retirement. Our findings indicate that a withdrawal plan following this rule (“4R”) carries an historical risk of failure for a long retirement that is much higher than generally acknowledged. For example, we find that 15% of the historical 35-year retirements failed when funded with equal parts of stocks and bonds. The “real” withdrawal plans that generated no historical failures were all less than 4%, sometimes far less, when retirements exceeded 25 years. The historical failure rates that we find for a 5R plan are higher than a 4R plan by a factor of at least three for all retirement periods. The historical failures are not random. Rather they occur in clusters of years in which the majority of new retirement withdrawal plans fail. A key driver of these failures was a rapid, significant and lasting increase in the rate of inflation - this event increased withdrawals and contributed to a declining real rate of return that was ultimately unable to support the withdrawal plan. Although TIPS bonds and inflation-adjusted annuities are both too new for historical analysis, we note they may offer an opportunity to curtail income plan failures in the future. This is because they (1) offer a known real rate of return and (2) adjust for inflation close to the time at which inflation impacts withdrawals. Our review of the prior literature and a detailed description of the methodology used in the study appear at the end of the paper, after the Summary and Conclusions section.

The Success Rate of New Drug Development in Clinical Trials: Crohn’s Disease

To determine the risk of drug failure during clinical trial testing in Crohn's disease and determine what steps can be taken to improve outcomes. This is the first study to quantify such risk for a single disease. Moderate to severe Crohn's disease was investigated by reviewing press releases from 1998 to June 2008. Clinical trial failure causes were classified as commercial or clinical and compared with industry expectations. The risk of failure was also reviewed based on whether the compound was a small molecule drug or a biologic. Lastly, the role of the sponsor was examined, in determining whether the size of the firm involved in a drug program was predictive of the outcome of the study. More than a 120 press releases were reviewed yielding 37 drugs that met our search criteria. The cumulative success rate for drug development in Crohn's disease is 19%, from start to finish of clinical trial testing. New drug approvals are dominated by protein based therapeutics in this indication. Commercial and clinical failures both contributed substantially to the failure rates of new drugs. Phase I clinical testing appeared to offer little risk mitigation with pass rates at 95%. Funding intended to advance Crohn's disease must take into account the disease specific historical failure rate of drug development in forecasting any reasonable expectation of producing new therapies. As it currently stands, one in five drugs will be successfully approved that enter clinical trial testing in this indication. To manage this risk continued development of biologics over small molecule drugs may be warranted in this disease.

Risk-averse reliability optimisation in electronic product design with component and non-component failures

Reliability prediction assists manufacturers to eliminate product failures in the development stage and also allocate engineering resources to implement corrective actions after product shipment. This paper proposes a practical reliability prediction model for estimating failure rates of printed-circuit-boards during the development stage. Unlike traditional reliability prediction models focusing only on component failures, the new method further considers non-component failures due to design, software, manufacturing and process. Component failure rates are estimated based on either historical data or baseline failure rates adjusted by operating conditions. Triangular distributions are used to model non-component failure rates. Finally, an optimisation problem is formulated in order to minimise the upper bound of the failure rate under cost constraints. A genetic algorithm is used to search the optimal solution. The optimisation method is demonstrated by the design of a DC/analogue instrument board used in the automatic test equipment in semiconductor industry.